1. Technical Field
The present invention relates in general to business process transformations, and in particular, to a method and system for optimizing business and work processes utilizing intelligent feedback mechanisms.
2. Description of the Related Art
Improving existing business and work processes is a critical element of business management for an organization. A business process or business method is a collection of interrelated tasks, which solve a particular issue. There are three main categories of business processes: management processes, operational processes, and supporting processes. Management processes govern the operation of a system. Examples of management processes include corporate governance and strategic management. The second main category of business processes, operational processes, constitutes the core business and creates the primary value stream. Typical operational processes are purchasing, manufacturing, marketing, and sales. Supporting processes, the last main category, are those processes that support the core processes. Examples of supporting processes include accounting, recruitment, and Information Technology (IT)-support.
A business process can be decomposed into several sub-processes, known as work processes, which have their own attributes and tasks, but also contribute to achieving the goal of the super-process (i.e., the business process). Thus, the analysis of business processes typically includes the mapping of processes and sub-processes down to task level. As business processes and their associated work processes become more complex, it becomes necessary for business planners to create and run computer-based models such that a more accurate business outcome can be derived based on a particular business value proposition. While it would be highly desirable for these models to take into account all possible input variations (i.e., business value propositions), the reality is that existing models are limited in practice to the number of variables and the combination of those inputted variables. Practically speaking, running such complex business models could take weeks, if not months to yield an optimized business solution.
Under existing methods, the interaction between business value propositions is typically explored and simulated using a brute-force full factorial method or a Monte Carlo exploration of the input combinations (which may run in the hundreds of thousands of combinations). A Monte Carlo method is a computational algorithm that relies on repeated random sampling to compute its results. Because of their reliance on repeated computation and random or pseudo-random numbers, Monte Carlo methods are most suited to calculation by a computer. Monte Carlo methods tend to be used when it is infeasible or impossible to compute an exact result with a deterministic algorithm. The pattern is typically as follows: a domain of possible inputs is defined, the possible inputs are generated randomly from the domain, a deterministic computation is performed on the generated inputs, and the results of the individual computations are aggregated into the final result.
However, there are several disadvantages associated with these existing methods. First, neither the brute force method nor the Monte Carlo method is capable of performing self-correction of the input variables without human intervention. Second, current methods fail to account for every new business values proposition that is modeled and modified to check for the modeled outcome. In this regard, current business transformations seeking to meet new market and technology opportunities rely primarily on human intuition, which is based largely upon the businessperson's perceived experiences, as well as the evaluation of business study cases. As a result, the above processes are primarily qualitative in nature.
The present invention therefore recognizes that it would be useful and desirable to establish a formalized method and system for generating an optimized analytical business transformation.